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Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Sep 28, 2023; 29(36): 5211-5225
Published online Sep 28, 2023. doi: 10.3748/wjg.v29.i36.5211
Integration and implementation of precision medicine in the multifaceted inflammatory bowel disease
Gowthami Sai Kogilathota Jagirdhar, Department of Medicine, Saint Francis Health Science Center, Newark, NJ 07107, United States
Jose Andres Perez, Department of Medicine, Saint Francis Health Systems, Tulsa, OK 74133, United States
Andrea Belen Perez, Department of Research, Columbia University, New York, NY 10027, United States
Salim Surani, Department of Medicine and Pharmacology, Texas A&M University, College Station, TX 77413, United States
ORCID number: Gowthami Sai Kogilathota Jagirdhar (0000-0003-1855-0863); Jose Andres Perez (0009-0007-2337-6317); Andrea Belen Perez (0009-0002-1547-3516); Salim Surani (0000-0001-7105-4266).
Author contributions: Jagirdhar GSK, Perez JA, and Perez AB performed the literature review, wrote the original manuscript; Surani S wrote the original manuscript, revised the paper, and approved the final version.
Conflict-of-interest statement: None of the co-authors declared any conflicts of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Salim Surani, FCCP, MD, MHSc, Academic Editor, Professor, Department of Medicine and Pharmacology, Texas A&M University, 400 Bizzell Street, College Station, TX 77413, United States.
Received: July 1, 2023
Peer-review started: July 1, 2023
First decision: July 10, 2023
Revised: July 31, 2023
Accepted: September 6, 2023
Article in press: September 6, 2023
Published online: September 28, 2023


Inflammatory bowel disease (IBD) is a complex disease with variability in genetic, environmental, and lifestyle factors affecting disease presentation and course. Precision medicine has the potential to play a crucial role in managing IBD by tailoring treatment plans based on the heterogeneity of clinical and temporal variability of patients. Precision medicine is a population-based approach to managing IBD by integrating environmental, genomic, epigenomic, transcriptomic, proteomic, and metabolomic factors. It is a recent and rapidly developing medicine. The widespread adoption of precision medicine worldwide has the potential to result in the early detection of diseases, optimal utilization of healthcare resources, enhanced patient outcomes, and, ultimately, improved quality of life for individuals with IBD. Though precision medicine is promising in terms of better quality of patient care, inadequacies exist in the ongoing research. There is discordance in study conduct, and data collection, utilization, interpretation, and analysis. This review aims to describe the current literature on precision medicine, its multiomics approach, and future directions for its application in IBD.

Key Words: Precision medicine, Multiomics, Inflammatory bowel disease, Crohn’s disease, Ulcerative colitis, Data integration

Core Tip: Precision medicine holds significant promise in managing inflammatory bowel disease (IBD) by aiming to customize treatments based on the unique biological characteristics of patients. Despite advancements in biological and small-molecule therapies offering new therapeutic options for IBD; however, the is a large gap in understanding the clinical course of IBD, the durability of treatment response, and enhancing available and new therapeutic options. Integrating multiomics into patient care can help in early diagnosis, predict disease course, deliver targeted treatments based on unique patient profiles, and evaluate prognosis.


Understanding precision medicine requires knowing two important concepts. (1) The definition of precision medicine; and (2) The role it plays in inflammatory bowel disease (IBD).

According to the National Cancer Institute, precision medicine is a form of therapy that utilizes knowledge about an individual’s genetic makeup or specific proteins to prevent, diagnose, or treat diseases[1]. Liu et al[2] state the term was first proposed in 2011, and over the years, this term has gained more importance. The strategy of precision medicine is to classify patients with shared characteristics into the same subgroup based on specific and similar clinical features, treatment, and prognostic factors[2].

The pathophysiology of ulcerative colitis (UC) and Crohn’s disease (CD) is increasingly evolving, mainly focusing on its genome, exposome, microbiome, immunome, and many other omics approaches. Thomas et al[3] noted that the development of IBD is attributed to an aberrant immune reaction resulting from intricate interactions among various genetic risk factors, imbalanced gut flora, and environmental influences. Unlike rare and clearly defined monogenic disorders, IBD arises from mutations in multiple genes rather than a single gene[3]. In this sense, precision medicine is pivotal in creating personalized therapies combining these various aspects into a multiomics approach[4]. If we target only the immune part of it without addressing the genome, the microbiome can result in treatment-limited success[2]. By treating IBD patients with the same and limited therapies, we assume that the underlying gastrointestinal inflammatory process is the same in all patients. Precision medicine in IBD aims to utilize specific clinical and biological characteristics of patients to predict disease and therefore use the correct treatment at the right time for the right patient[5].

The three priority areas of the precision medicine approach to IBD are: (1) Understanding the vulnerability, severity, and behavioral aspects of disease; (2) forecasting the response to medications; and (3) enhancing existing and creating novel molecular technologies to facilitate precision medicine.

We can achieve this through advances in multiomics in IBD. Figure 1 shows the various omics in IBD management. Using this approach, we could better understand disease pathogenesis, model predictive biomarkers, and facilitate early diagnosis and treatment.

Figure 1
Figure 1 The interplay of various omics in inflammatory bowel disease.

We conducted a review of existing literature in PubMed and Google Scholar to find relevant studies with information on multiomics in IBD and integrating various IBD therapies. We included studies from inception to May 2023. We created search criteria using a combination of free text words, including precision medicine, IBD, Genome, epigenome, epigenetics, proteome, transcriptome, immunome, microbiome, and integration. We examined studies that were published in the English language as part of our review. Pertinent articles were analyzed and incorporated into the review.


Genetic factors stratify IBD into UC and CD. The utilization of genome-wide association studies (GWASs) has enabled the identification of notable genetic risk regions and IBD phenotypes to classify the disease into ileal Crohn’s, colonic Crohn’s, and UC compared to the current classification[6]. The genetic mechanisms implicated in IBD encompass various pathways, including microbial sensing (NOD2, CARD9 and RIPK2), maintenance of intestinal barrier function (C1orf106 and HNF4A), signaling within the innate and adaptive immune systems (NLRP7, IL18RAP, CD28, IFNG, PTPN22, STAT4, IL6ST, IL23R, RORC and IL17RA), fibrosis development (OSMR and SMAD3), and cellular homeostasis (ATG16L1, RNF186 and ERGIC1)[7]. Despite the advances in genetic pathways, few relate to treatment response, prognosis, or clinical outcomes. Over 230 genetic loci have been identified to be involved in IBD[2,6,8]. However, in one of the largest phenotype-genotype GWASs of 29838 patients by Cleynen et al[6], over 163 loci related to IBD were studied, and genetic risk scores were created for CD and UC separately. On cross-validation of the data, the predictive accuracy for phenotypic variance in the adult population was low. These genetic predictors, along with smoking, could only explain 6.8% of the disease location with CD and only 1.1% with UC emphasizing that research using an interdisciplinary approach is required to provide the best risk assessment and aid diagnosis[6].

GWASs have been used in investigating pathways like the NOD2/CARD15, which are involved in pattern recognition and receptor signaling in response to microbial signaling. It has been associated with an ileal fibrostenotic disease phenotype that needs surgery and has a complicated disease course[9]. There is another study of the ATG16L1, T300A, an autophagy pathway reported to give an enhanced risk of CD[10]. Another autophagy gene IGRM is associated with penetrating disease in CD[11]. Interleukin (IL)-3 receptor has become a therapeutic agent for CD[12]. Studies have utilized genome basis to identify patients that benefit from hematopoietic stem cell transplantation (HSCT) and mesenchymal stem cell transplantation in patients with early-onset IBD and those with inborn errors of immunity[13-16]. Patients with genetic deficiencies such as IL-10, IL-10 receptor, and XIAP deficiencies benefitted from HSCT[13,17,18]. It also identifies mutations such as STXBP2, TTC7A, and EPCAM genes that will not benefit from particular therapies[19-21]. IBD is a polygenic disease, and research on the utilization of genome pathway-specific therapies to correct genetic defects is still developing. Thus, understanding genetic mechanisms helps personalized medicine in IBD.


Epigenetics is the study of inheritable changes in gene expression that are not caused by alterations in the DNA sequence.

Environmental factors cause alterations in DNA methylation, histone modifications, and miRNA synthesis, thus changing the phenotypic expression of genes and response to treatment. Parenteral exposure influences epigenetic alterations that are transmissible across generations. Epigenetics factors are potentially modifiable and preventable. Maternal protein restriction and maternal high fat intake were associated with DNA methylation changes that led to increased steatosis, impaired glucose tolerance, diabetes, and obesity in offspring[22,23]. Pan et al[24] describe gut microbiota alteration that causes changes in DNA methylation and transcriptome changes in intestinal epithelial cells postnatally in newborns[24]. A prospective study by Sun et al[25] describes a 12-year follow-up of CD and UC patients. Patients with unhealthy lifestyles had a hazard ratio (HR) of 1.94 and 1.98 for CD and UC. Patients with increased genetic risk for IBD but healthy lifestyles had a lower risk of developing IBD than those with increased genetic risk and unhealthy lifestyles (HR: 2.23 vs 4.40)[25]. Yang and Jostins-Dean[26] published data using the information of IBD patients from a United Kingdom biobank. They analyzed 24 dietary exposures, perinatal childhood exposures, and lifestyle factors in individuals. They showed that multiple factors interacted, causing polygenic risk in predicting IBD occurrence. Factors such as appendectomy, smoking, childhood antibiotic use, exposure to the sun in winter, socioeconomic factors, and oral contraceptive use played a role in IBD pathogenesis[26]. Ryan et al[27] found that inflamed and non-inflamed colonic mucosa in IBD patients had different microbiota composition and epigenetic profiles. Using machine learning to classify disease status and inflammation, the authors found that epigenomics and microbiota could classify UC, CD, and health status with an AUROC of 0.87[27]. Thus, epigenetics plays a role in defining IBD risk.


The exposome is defined as all encountered exposures in the lifetime of an individual beginning from conception. There are endogenous and exogenous exposomes in IBD. Microbiota is an endogenous exposome. Evidence suggests that mode of delivery at birth (cesarean vs vaginal, breastfeeding vs formula feeding) and early exposure to antibiotics in life are associated with intestinal colonization of bacteria early in life[28,29]. They contribute to intestinal immune homeostasis and are associated with UC and CD development[28]. Direct effects of infections, stress, diet, air and water pollutants, food additives, antibiotics, vitamin D deficiency, and physical activity on intestinal mucosal cells and indirect effects through alterations in the microbiome and immunomodulation influence IBD occurrence, presentation, course, and response to treatment[30,31]. These factors have been shown in animal models to induce DNA methylation and alteration in RNA and histone proteins, promoting the development of IBD[31]. Notably, Borg Bartolo states that industrialized nations exhibit the highest incidence of IBD[4]. According to Lamb et al[32], it is the environmental factors that influence IBD pathogenesis. A western diet of fast and processed foods, high-fat content, ultra-processed food consumption, and meat increases the risk of developing disease. In contrast, mitigating elements comprise elevated consumption of dietary fiber, caffeine, fruits, vegetables, olive oil, seafood, cereals, and nuts; all of which play a crucial role in safeguarding against certain conditions[32]. Initial investigations have demonstrated that exclusive enteral nutrition (EEN) diets based on specific formulas can effectively trigger remission in individuals with CD. As per the findings of Svolos et al[33], a customized food-based diet has been developed, sharing a similar composition with EEN. This personalized diet has exhibited favorable tolerability, notable enhancements in disease activity, significant alterations in the gut microbiome, and a metabolome profile comparable to that achieved with EEN[33]. Smoking, for example, is a risk factor in the progression of CD[34]. The relationship between IBD and smoking varies for CD and UC. Chemicals contained in the smoke generate carcinogenic and mutagenic components that can affect the immune system in the gastrointestinal mucosa. Smoking releases reactive metabolites that can cause DNA methylation, thus affecting gene regulation and expression[28]. Epigenome and exposome are closely interlinked in IBD pathogenesis.


The gut is the main barrier that controls the immunological interface and maintains the immunological balance by appropriately recognizing and tolerating bacteria, food antigens, and self-antigens. Proteomics studies can develop targeted therapy for IBD and find biomarkers to monitor drug response in IBD patients. Yau et al[35] describe that blood tests can identify epithelial component proteins that help in early predicting intestinal complications in IBD, such as stricturing and fistulizing disease[35]. Deeke et al[36] describe the identification of active IBD and pancolitis based on a panel of proteins in IBD stool samples[36]. IBD tissue samples can be classified into UC or CD based on proteomic analysis[37-39]. Protein analysis also helps to understand the response to treatment therapies[40]. Calprotectin is a marker that can be used for mucosal healing and clinical remission in IBD[41,42]. Tumor necrosis factor-like ligand 1A (TL1A) is involved in colonic inflammation and fibrosis through epithelial-to-mesenchymal cell transition and immune response mechanisms. It was detected in high levels in IBD patients. This is a potential target for IBD treatment[43]. Since IBD is an immune-mediated disease, both CD and UC have primarily immune targets for treatment. Masoodi et al[44] described altered polyunsaturated-fatty-acid-derived lipid mediators (eicosanoids), such as PGE2, PGD2, TXB2, 5-HETE, 11-HETE, 12-HETE and 15-HETE, that are elevated in inflamed mucosa in IBD patients and correlated with the severity of inflammation. These can assist in diagnosing inflammation in IBD[44,45]. Bennike et al[46] describe increased levels of neutrophils and neutrophil extracellular traps and several other proteins in UC patients’ normal-appearing colon tissue, suggesting the role of innate immune system in IBD[46]. Titz et al[45] identified 18 studies on proteomics and lipidomics to identify biomarkers for IBD. Through the utilization of blood, serum samples, and colonic mucosal biopsies, these studies aided in diagnosis, patient stratification, treatment categorization, and response[45].


The microbiome is a critical factor in initiating and perpetuating the inflammatory response of IBD. Studies conducted on patients initiating treatment with vedolizumab have observed a higher prevalence of Roseburia inlinivorans and a species belonging to Burkholderiales among individuals with CD who successfully achieved remission by week 14[47]. Furthermore, a study indicated that neural networking modeling demonstrated superior predictive capability for remission when utilizing microbial metadata compared to relying solely on clinical metadata. In 232 patients in the CERTIFIED study treated with ustekinumab, Faecalibacterium and Bacteroides were found in baseline stool in CD patients who achieved remission[47]. Microbiome has also helped identify high-risk individuals. Gevers et al[48] discovered that specific microbial patterns in the ileum could accurately predict the occurrence of CD, even in cases where inflammation was not present[48]. Vázquez-Baeza et al[49] describes that microbes play an important role in altering the response to medications, indicating that altering them could modify a patient’s response to treatments. This can be a future target in microbiomics research[49].

Precision medicine needs to be applied by taking each of the above aspects and unifying them. The concept is to integrate data and generate patient profiles that could predict disease risk, onset, progression, complications, prognosis, and treatment in multi populations based on multiomics. Figure 2 describes a simplified pathway for utilizing multi-omics in IBD.

Figure 2
Figure 2 Simplified pathway to precision medicine in inflammatory bowel disease. IBD: Inflammatory bowel disease. Parts of Figure 2 were drawn by using pictures from Servier Medical Art. Servier Medical Art by Servier is licensed under a Creative Commons Attribution 3.0 Unported License (

The true power of precision medicine resides in the vast possibilities presented by multiomics data and its ability to merge effectively with clinical information. This concept is already applied in the field of oncology, where accurate biomarkers are utilized to forecast prognosis and anticipate the response to treatment[10]. The field of medicine has shifted from a treatment-oriented approach to a personalized and preventive approach. Biological data acquisition and digitization techniques have advanced, producing large amounts of high-specificity data. To examine and interpret these data, new databases, algorithms, and user-friendly applications are being designed to facilitate the analysis by bioinformatics specialists and physicians[5,50]. Table 1 shows studies utilizing multiomics in IBD for diagnosis and treatment.

Table 1 Studies utilizing multiomics in inflammatory bowel disease.
Type of study
Number of participants
Type of omics utilized
1Lloyd-Price et al[75]Cohort study132 IBDMicrobiome; genome; metagenome; meta; transcriptome; metabolome profiles; proteomeFound functional dysbiosis in the gut microbiome, changes in metabolite pools, and increased levels of antibodies in serum during periods of inflammatory bowel disease activity
2Mills et al[83]Cohort study-40 UC, -validation cohort: 73 UC, 117 CD, 20 controlsProteome; metaproteome; metabolome; metagenome; meta peptidomeUC patients with active disease had increased protease activity that originated from the bacterium Bacteroides vulgatus
3Zhang et al[84]Cohort study25 CD, 22 UC, 24 controlsMetaproteome; microbiomeIdentified functional alterations of microbiome and associated microbial proteins in the intestinal mucosa in new-onset pediatric IBD patients which contributed to abnormal host-microbiota interactions
4Lee et al[74]Prospective study108 CD, 77 UCMetabolome; microbiome; metagenome; proteomeMicrobial, metagenomic diversity and metabolomics (serum secondary bile acid concentrations) predicted response in moderate-to-severe Crohn’s disease or ulcerative colitis patients initiating anti-cytokine therapy (anti-TNF or -IL12/23) or anti-integrin therapy
5Dai et al[85]Prospective study5956 CD, 21770 controlsGenome; epigenome; transcriptomeIdentified strongly associated CD genes involved in immune response, MHC class II receptor activity, and immunological disorders. Involvement of constitutive photomorphogenesis 9 signalosome in CD pathogenesis. Interactions among CD genes with CD drug target genes
6Suskind et al[86]Randomized controlled trial10 CDMicrobiome; metagenome; metaproteome; metabolomeDiet had a positive effect on CD patient symptoms and inflammation; Specific exclusionary diets were associated with a better resolution of inflammation
7Borren et al[87]Cohort study108 CD, 56 UCProteome; metabolome; microbiomeIdentified proteomic, metabolomic, and microbiome biomarkers that characterized a proinflammatory state in quiescent IBD patients and predicted clinical relapse
8Liu et al[88]Prospective study42 UC, 11 controlsMetabonome; microbiomeIdentified that the combination of Verrucomicrobiota, butyric acid, and isobutyric acid improved the prediction of early remission to anti-integrin therapy
9Huang et al[89]Cohort study140 CD, 73 UC, 86 controlsMetagenome; meta transcriptome; proteome; metabolome; virome; microbiomeEstablished artificial intelligence models that can subtype IBD with high accuracy using simple noninvasive fecal biomarkers and avoiding invasive procedures
10Mishra et al[60]Cohort study37 IBDGenome; epigenomeIdentified biomarkers of early shifts in gene expression and DNA methylation for early prediction of anti-TNF treatment success
11Gonzalez et al[90]Cohort study200 IBDMicrobiome; metabolome; metaproteome; metagenomeIdentified colonic- and ileal-isolated CD subtypes had different pathologies. Colonic CD was similar to ulcerative colitis with neutrophil and Bacteroides vulgatus involvement while Ileal-isolated CD had a bile acid-driven profile
12Xu et al[91]Cohort studyDiscovery: 6 datasets, Validation: 46 CD, 44 controlsGenome; transcriptome; Epigenome; microbiomeIdentified that CD onset results from a number of oxidative stress genes through DNA methylation, gene expression, and host-microbiota interactions
13Gao et al[92]Cohort study30 CD, 5 controlsProteome; microbiome; metagenomeIdentified alterations in proteins, microbiome, and metabolic processes across multi-tissues in CD patients that are imprinted in serum and fecal samples as potential diagnostic biomarkers, allowing a non-invasive precision diagnosis
14Baran et al[93]Cohort study18 IBDEpigenome; transcriptome; genomeTherapeutic response causes transcriptional changes in the mucosa and early changes in gene expression in peripheral blood that are regulated epigenetically

To enhance transparency and accessibility of bio-sample data, standardization and harmonization of data frameworks are needed at a global level. A Data Commons should be established to facilitate global collaboration and provide access to biospecimens and data before and after treatment with established and novel therapies[3,51].

Cleynen et al[6] conducted a large genotype association study in patients with IBD using genotype-phenotype associations from 156-154 gene variants. They found ileal CD, colonic CD, and ulcerative colitis as distinct groups based on genetic risk scores of 29 838 patients. The application and utilization of these groups in clinical practice and patient treatment is not yet studied in detail[6]. Epigenetic studies by authors like Sun et al[25] prove the importance of genome-wide associations and gene–environment studies in IBD[25]. There is a need for biobanks such as the Dutch IBD biobank[52] and the GETECCU Eneida registry to be established globally since genetic predisposition, varied environmental exposures, and population differences can create different genetic susceptibilities based on location[53]. These can create population-specific effects that are not generalizable to all IBD patients[28]. This will create patient profiling that can be distinct based on geographic location. Studies conducted using biobank information also need to be standardized with quality control and the establishment of core validation guidelines so that generated results are comparable across studies and can be utilized to further precision medicine in IBD. Multiple studies analyzing biomarkers in IBD have been published, but the results have not been translated to clinical practice. We are lagging in terms of translation of the discovered research[45].

Screening programs such as DNA or RNA sequencing can inform healthy people with a family history of cancer about their health status. For example, mutations in the BRCA genes can be detected to identify the familial risk of breast cancer[54]. Disease monitoring using a combination of clinical data, biomarkers, imaging, therapeutic drug monitoring, modeling, and simulation is essential. The objective is to administer the appropriate treatment to each patient precisely when it is needed, based on their unique disease pattern and progression. By doing so, aiming to minimize long-term complications and improve patient outcomes[55]. The ability to identify the correct personalized therapies should also result in an earlier clinical response compared to current treatment strategies.

Omics are fields of study that analyze biological molecules and systems on a large scale. They aim to understand the complex interactions and functions of these molecules and systems and how they contribute to the health and disease of organisms. The study of genomics and transcriptomics is crucial for discovering disease pathways and designing effective drugs. Understanding these areas can help treat diseases that do not respond to current treatments. Nucleic acid sequencing methods, particularly next-generation sequencing technologies, are essential for studying genomics and transcriptomics. Various studies have explored individual differences in patients with the same condition at the genome level, including the human genome project, single nucleotide polymorphism detection, and GWASs. For the transcriptome, splicing, alternative splicing, and RNA sequencing are critical areas because many human genes undergo alternative splicing, and changes at this level can be difficult (and usually impossible) to detect at the genome level[54].

IBD omics investigate particular characteristics associated with IBD, emphasizing the importance of using methods that thoroughly explore and model the various interactions at play, enabling the acquisition of comprehensive knowledge. The use of molecular markers or molecular disease profiles can eliminate the interpretive aspects of response and remission definition and can guide more accurate drug selection and monitoring of immune modulators, biologics, and small molecule therapy[3,51,56].

The primary obstacle in the development and validation of precision medicine interventions for IBD lies in the extensive variability observed in disease progression, coupled with the current inability to accurately predict its course. Predictive models need to account for environmental influences and the heterogeneous response to treatment over time. Although mucosal healing serves as a substantial indicator for predicting future relapse, complications, and the likelihood of surgery, the requirement for frequent endoscopic examinations can be resource-intensive and may not be well-received by patients. To address this, patient-reported outcomes, and noninvasive biomarkers like fecal calprotectin (FC) and C-reactive protein (CRP) are highly utilized to assess treatment response in both research studies and clinical practice. However, the lack of universally agreed-upon definitions for treatment response and remission poses challenges in interpreting the usefulness of these measures[4,32].

A systematic analysis of six studies showed that two consecutive elevated FC levels predicted disease relapse within the next 2-3 mo, but an optimal cutoff for monitoring could not be defined. However, it was discovered that setting a cutoff value of no more than 250 μg/g for FC could effectively predict the achievement of endoscopic remission. Likewise, it was observed that CRP exhibits a correlation with both clinical disease activity and endoscopic inflammation. However, it is important to note that CRP levels can also be elevated in other clinical situations, such as infections. Glycoprotein acetylation (GlycA) is a new biomarker that has been investigated as a potential disease activity biomarker in patients with IBD. GlycA presents a potential advantage over CRP due to its composition as a composite marker derived from various acute-phase proteins. This characteristic renders GlycA more stable compared to CRP[4].

Two studies in 2021 shed light on the development and treatment of IBD[50]. The first study investigated the role of NOD2 variants in the pathogenesis of CD and found that loss-of-function mutations in NOD2 were associated with the activation of a macrophage–fibroblast niche, which is involved in the development of the disease. The study also identified a potential signaling pathway involving the gp130-STAT3 axis, which may contribute to the pathogenesis of CD. Furthermore, the study found that the gp130 inhibitor bazedoxifene could alleviate pathogenicity in zebrafish models of IBD. The second study aimed to predict the effectiveness of treatments for patients with IBD by analyzing microbial community profiles, baseline clinical features, and serum inflammatory markers. The study found that patients who started with anticytokine therapy had higher remission rates compared to those who began with anti-integrin therapy. Additionally, the study demonstrated the potential for using multiple types of data, including metagenomic, metabolomic, and proteomic features, to predict therapeutic efficacy for patients with IBD[5,50]. Rapid advances in human microbiome research suggest that profiling and manipulating the human microbiome can offer significant opportunities for diagnosis, intervention, risk management, and risk stratification[2].

Discussions about the integration of research and clinical care have focused more on human genetic and genomic data than human microbiome data. In 2014, working groups were established to explore the storage and display of genetic information in electronic medical records, which revealed differences in how genetic information was documented across institutions and the need to develop effective clinical support mechanisms for standardized clinically actionable genetic information that can be shared across health systems.

Through research, we have been able to identify environmental exposures in IBD, but we need to improve integrating it into patient profiles. Planned investigations with multinational studies in the field of both basic laboratory science and epidemiological studies will help create extensive patient profiling that can be used in understanding disease processes and further patient care[28]. According to Valles-Colomer et al[57], the use of microbiology (metaomics) has allowed the analysis of bacteria present in people with IBD. In normal flora, the use of 16S rRNA sequencing identified Bacteroidetes and Firmicutes, interestingly in people with IBD, gastrointestinal biopsies have found decreased levels of these bacteria[57].

According to a study by Stankovic et al[58], the use of machine learning approaches by using artificial intelligence (AI) and algorithms creates patterns not seen before in the management of IBD[58]. By creating big datasets and then using linear regressions, logistic regressions, and fitting models to characterize risk factors, patient baseline characteristics, treatment plans, and prognosis can be predefined. To date, the largest database available for IBD is the IBD genomic database, which allowed the discovery of more than 200 IBD-related loci[57].


The ability to predict if a treatment will fail is important as well, as it allows for early switching of therapy and improves the chances and cost-effectiveness of recapturing a positive response. The Personalized Anti-tumor necrosis factor (TNF) Therapy in CD Study (PANTS) followed 1610 patients on anti-TNF therapy for 12 mo or until therapy was withdrawn. The presence of low drug concentrations at week 14 demonstrated a connection to both primary nonresponse and the absence of remission at week 54. Patients on infliximab were more likely to have immunogenicity than those on adalimumab (62.8% vs 28.5%). Obesity at baseline was linked to nonremission at week 54 for adalimumab only, while smoking and immunomodulator use affected infliximab. HLA variants and glycosylation also affected anti-TNF therapy immunogenicity. Colonic biopsies obtained from patients diagnosed with UC revealed reduced levels of branched N-glycans, which served as an indicator of the inability to respond effectively to standard therapy. High levels of CRP were identified as the sole additional independent predictor in this context[59]. Single-cell gene expression technologies have been used to gain insights into the molecular mechanisms underlying drug response to comprehend the pathophysiology of this process. In cases of active UC, there is a distinct decrease in the expression of mitochondrial genes in the epithelial cells. Conversely, an exclusive cellular module in CD is observed in inflamed tissues, which is linked to the inability to achieve long-lasting remission with corticosteroids and anti-TNF therapy[4]. Not surprisingly, one study by Mishra et al[60], which analyzed the early shifts of gene expression and DNA methylation, found that patients resistant to TNF treatment lacked such signatures[60].

Single-cell profiling techniques can help identify and measure different cell populations and their transformations over time and location. Changes in cell behavior can disrupt the balance in IBD, and studying these changes using techniques like single-cell RNA-seq and ATAC-seq can help understand the complex spectrum of cell types that contribute to IBD pathogenesis[50]. In 2021, researchers developed yeast-based probiotics for the treatment of IBD. The engineered probiotics expressed a human P2Y2 purinergic receptor that senses extracellular ATP, a molecule involved in intestinal inflammation. Activation of the receptor led to the secretion of the ATP-degrading enzyme apyrase, which created a self-feedback mechanism to maintain extracellular ATP at normal levels. The engineered probiotics did not cause unwanted adverse effects such as inflammation-driven fibrosis or gut microbiota dysbiosis. This shows promise for the development of new therapies for IBD based on precision medicine approaches that target the gut microbiota[5,50].

Multiomics projects are currently underway to investigate the heterogeneity of IBD and improve precision management. However, these high-throughput data require advanced computational techniques, such as machine learning to model and analyze them. Machine learning includes supervised and unsupervised algorithms that could be used for patient clustering, predictions, and to detect novel biomarkers. These strategies have been successfully applied to integrated multiomics data in cancer therapy[2]. Incorporating the increasing array of biomarkers, prediction tools, and treatments into the field of IBD presents a notable difficulty. The combination of early intervention and these novel tools may provide the greatest opportunity for delivering precision medicine in IBD. Additionally, powerful tools and equipment are required to store and share a vast amount of data. The use of mobile phones and app technology is one of the significant advances in this field for collecting and sharing big data. However, knowledge and expertise are necessary to standardize the collection of data and translate the archived data into clinical applications. It is our responsibility to contribute to big data in IBD and practice high-quality biomarker-driven care to enable the provision of truly individualized patient care in the future[4,54,55].

Molecular profiling is another technique that can provide information on the genes involved in IBD. It is based on tissue and blood and can predict specific patterns. For example, one study showed which patients were responders versus nonresponders to anti-TNF induction using single transcripts such as the OSM, OSMR or TREM[61]. A study also differentiated responders versus nonresponders to vedolizumab based on the expression of RGS13, DCHS2, MAATS1 and P1Wil1[56]. The PROFILE trial marks a groundbreaking advancement in medical research, being the inaugural prospective randomized control trial that used T-cell signatures to categorize patients at the time of diagnosis[62].

Deep learning models using convolutional neural networks, computer image analysis systems, and machine learning can predict disease severity and remission comparable to that of gastroenterologists and radiologists[63-65].

Waljee et al[66] used machine learning to predict steroid use and the risk of hospital admission in IBD patients. Developing such models can help in the therapeutic management of IBD patients[66]. Lee et al[67] describes 170 disease susceptibility loci in IBD and found that loci associated with the risk of developing IBD were not predictive of disease prognosis. This emphasizes the need to reassess GWASs based on patient phenotypes, response to treatment, and prognosis[67].

Finally, treatment with biologics can alter the transcriptome profiles of CD patients distinguishing naïve from exposed patients undergoing ileocolic resection. This knowledge can be used by clinicians in medical therapy decision-making postoperatively in adjunct to clinical factors and endoscopy[68].

Integrative approaches combining multiple omics to create a framework that assesses the complex factors that interplay in IBD are required. This should be established from the initial risk assessment, prevention of disease, disease categorization, subcategorization, treatment during the changing disease course, prognostication, and follow-up.


In recent decades, advances in technology have enabled the comprehensive profiling of the human gut microbiome through metaomics methodologies. The power of functional metaomics lies in its potential to identify molecular biomarkers that can assist with noninvasive screening and diagnosis, and treatment response. Data integration is the process of combining multiomics data[69,70]. Table 1 describes studies consulting using multiple layers of multiomics data. Recent comprehensive human omics studies have substantiated findings from murine models and enhanced our comprehension of the connections between bile acids, their microbial progenitors, intestinal inflammation, and tumorigenesis. However, a significant portion of these human omics studies predominantly focus on either metagenomics or bile-acid-related metabolomics rather than combining this data to elucidate the associations between fluctuations in the abundance or function of gut microbiota and bile acid concentrations[71]. There is an essential need for additional investigations to delineate the role of bile acids in the adaptive immune system, thereby facilitating the evolution of therapeutic agents and diagnostic tools for IDB and colorectal cancer (CRC). Targeting the gut microbiota–bile acid axis offers significant potential for CRC and IBD treatment and prognosis but requires extensive research to deepen our understanding of the mechanistic links and clinical implications[71].

The intestinal microbiota is increasingly viewed as a potentially influential factor in the onset of IBD and CRC. IBD and its association with CRC suggest a potentially shared etiology between these two conditions. It is suggested that genetic, environmental, and immunological factors play a role in the onset of these diseases; however, the mechanisms through which they exert their influence remain complex and ambiguous. Additionally, functional metaomics has revealed similar metabolic dysregulation in both IBD and CRC, suggesting a disruption in host–microbiota interaction leading to abnormal immune responses and inflammation[72].

In another study, omics data were utilized to profile prospective patients undergoing either anticytokine or anti-integrin therapy. Certain bacterial species linked to a decrease in inflammation, often by creating a beneficial fecal short-chain fatty acid profile, were associated with patients more likely to achieve remission with anticytokine therapy. Moreover, these patients had an increased presence of colonic-butyrate-producing species that promoted intestinal balance. Those undergoing anti-integrin therapy who successfully achieved remission demonstrated increased abundances of different bacterial species, such as Bifidobacterium longum and Bacteroides species. Interestingly, some of these bacteria have previously shown effectiveness in independent microbiome-based therapies. However, the correlation between the microbiome and clinical response was more significant in week 14 of remission than in week 52 outcomes, suggesting the microbiome’s influence may be on early response[73]. In this study, it was demonstrated that metabolomic profiling also contributed to understanding patient responses to treatment, with a strong association found between serum secondary bile acid concentrations and the likelihood of achieving remission with anti-cytokine therapy[74].

The Integrated Bowel Disease Microbiome Database study, part of the Human Microbiome Project Phase 2, investigated the gut microbiome of IBD. They used various measurement methods to analyze the microbiome's molecular components in CD and UC. The study found unique molecular features associated with dysbiosis in IBD, including previously known and new findings. The stability of the microbiome varied across IBD phenotypes and disease activity. The study identified potential targets for further investigation and highlighted the need to translate the findings into clinical applications, such as predictive biomarkers and new treatments for IBD[75].

The Danish IBD biobank project on 840 patients naïve to biologics started on biological therapy. It is an ongoing 3-year study utilizing clinical patient data, transcriptomic, proteomic, and microbiomic data. The study aims to identify biomarkers for response to biological therapy and understanding disease courses in IBD patients. The IBD prognosis study, 1000 IBD project, Swedish IBD quality register, and the Dutch IBD biobank are similar multi-omics integration studies and datasets for IBD[52,76,77].

The expansion of advanced platforms and tools for omics analysis signifies a change in molecular epidemiological research. Although the current omics data of IBD development is limited, the field is rapidly growing and presents immense possibilities. Various human cohorts, including those involving diverse births, at-risk individuals, and prediagnostic stages, have been established. Ongoing multiomics studies aim to characterize the pathways associated with IBD, incorporating the use of novel biomarkers. Rigorous statistical analyses, long-term monitoring of omics patterns in individuals before and after the onset of IBD, and replication of findings in independent cohorts are expected to provide valuable insights. To draw reliable conclusions from individual studies and facilitate the integration of data from multiple studies, it is crucial to collect detailed clinical information and follow standardized sampling, storage, and analysis protocols.

Availability of the knowledge acquired from precision medicine across all socioeconomic statuses is essential. Current data on precision medicine is not diverse and is restricted to some groups of populations. In a systematic review by Norris et al[78], those with low socioeconomic status were 17% less likely to be treated with novel precision medicine therapies for cancer[78]. Spratt et al[79] describes in his study of 5729 genomic sequencing samples that only 12% were African American and 3% were Hispanic and 3% were Asian. This over-represents the white population and under-represents other communities[79]. The results of such research can have an impact on the understanding of disease pathogenesis and the generalizability of results globally[80]. In a study by Yeh et al[81], African Americans and Hispanics questioned the benefits of precision medicine to their population due to the current inequalities in health care access and quality[81]. Another example of such a difference was seen by Lynch et al[82] in their study on Medicare patients with lung cancer. The authors observed racial, income, and regional disparities in EGFR testing and underutilization of guideline-recommended testing in certain populations. Precision medicine is advancing in terms of the complexity of disease management. The benefits of precision medicine need to be available to everyone equally, and a conscious effort to not increase the existing gap in health care is necessary[82].

Advancements in analysis techniques of omics data hold the potential to revolutionize the molecular epidemiology of IBD and other diseases, especially as more high-quality studies are conducted. This could lead to improved prediction of IBD onset and the development of treatment and preventive strategies. Furthermore, the findings in such studies can serve as a foundation to unravel pathways relevant to other immune-mediated and chronic diseases, contributing to advancements in public health as a whole[65].


With the upcoming evolution of AI to develop algorithms to diagnose and treat diseases, using databases will be the pillar of collecting information and analyzing trends with the help of statistical systems. It is essential to combine these individual databases so that by documenting known patient factors, specific profiles that will allow us to treat patients with specific patterns are generated. The complexity and heterogeneity of IBD make it a challenge because it is an evolving disease, and one treatment will not fit all patients. The future of IBD relies on these multiomics databases and the integration of profiles targeting different omics layers.

Establishing a checklist for future studies on precision medicine in IBD to regulate the methodology, quality, model validation, external validation, and algorithmic bias. Utilizing checklists to ensure reproducibility from study conduction to analysis and study data presentation will allow a better understanding of study outcomes and allow its applicability globally.

Efforts to minimize the current socioeconomic and racial gap in health care and precision medicine by ensuring further research include minority groups in diagnostics, drug discovery, and disease management.

Information about barriers to participation based on sex, race, and socioeconomic status is essential to help in the recruitment of underrepresented samples and ensure results are generalizable.

Precision medicine is promising, but the cost of genomic sequencing and creating patient profiles is not affordable to all patients. This could lead to further disparities in health care and is a barrier to its widespread adoption.

Large complex data needs to be standardized and integrated to achieve the full benefit of precision medicine. Establishing standards for collecting widely variable genomic data to allow comparison across studies is necessary.

Another important concept is the ethical aspects of utilizing genetic and patient data, ensuring patient privacy, and preventing data misuse.

There is a wide gap from creation to translation of the extensive research that is being generated on omics data. The complex information generated needs to be integrated and translated to patient care. This process involves rigorous clinical evidence, standardization, cost-benefit assessment, and regulations.

Summary of key points

Defining and understanding IBD requires a multidisciplinary approach by taking the genome, exposome, microbiome, immunome, proteome, transcriptome, epigenome, and metabolome concepts as a unit.

The development of GWASs has allowed the identification of notable genetic risk regions and IBD phenotypes. Other pathways have been developed, including microbial sensing, the maintenance of intestinal barrier function, signaling within the innate and adaptive immune system, fibrosis development, and cellular homeostasis.

Regarding the environmental risk factors, databases like the United Kingdom Biobank have allowed the recognition of polygenic risk factors like smoking, childhood antibiotic use, sun exposure in the winter, socioeconomic factors, and oral contraceptive use to be related to IBD occurrence.

The study of proteins has always been a target for diagnosis and treatment. One example is the TL1A, which has been involved in colonic inflammation and fibrosis through epithelial-to-mesenchymal cell transition and immune response mechanisms.

The interaction of our gut with the microorganisms that form part of the habitat in our intestine has also been the target of studies like the CERTIFIED study, where patients treated with ustekinumab, bacteria like Faecalibacterium and Bacteroides were found in baseline stool in CD patients who achieved remission.

The use of databases and AI creates the possibility of algorithms not seen before for the treatment and management of IBD, with the use of linear, logistic regressions and fitting models to characterize risk factors, baseline characteristics, treatment plans, and prognosis.


IBD is a complex longitudinal disease that utilizes multiple omics such as genomics, epigenomics, microbiomics, proteomics and immunomics. Utilizing mutiomics to further IBD management and deliver precision medicine with next-level care in IBD patients for the current and future should be targeted.


Provenance and peer review: Invited article; Externally peer reviewed.

Peer-review model: Single blind

Corresponding Author's Membership in Professional Societies: Society of Critical Care Medicine; American College of Physician.

Specialty type: Gastroenterology and hepatology

Country/Territory of origin: United States

Peer-review report’s scientific quality classification

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Grade B (Very good): B, B

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P-Reviewer: Duan SL, China; Wen XL, China; Yang JS, China S-Editor: Fan JR L-Editor: Kerr C P-Editor: Fan JR

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